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PDF to Mind Map

AI
Hierarchical outline of a PDF — 4–8 branches, 3 levels deep. Text + JSON export.
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What you'll get: an interactive mind map of your PDF's main ideas — central topic, branches per section, leaf nodes for key points. Exportable as PNG / SVG / Markdown. For another use, try AI · Study Notes.

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use AI Mind Map

Some docs are too sequential to read non-sequentially — you need the structure first. AI Mind Map renders a hierarchical outline of any PDF (4–8 root branches, 3 levels deep) so you can see the doc’s shape before reading. Output is text + JSON, importable into mind-mapping tools.

Textbook / course material study guide
Turn a 200-page textbook chapter into a 3-level mindmap and use it as the navigation index for studying. The hierarchy mirrors the book’s structure with chapter / section / sub-section depth.
Research literature review
When a paper covers multiple sub-topics (a methodology paper, a survey paper, or a long thesis chapter), the mindmap surfaces the conceptual hierarchy. Faster than re-reading to find a section.
Policy / procedure doc structure
Long policy docs (HR handbook, compliance manual, vendor security questionnaire) have nested structure that PDFs flatten. Mind Map restores the tree so you can find which section answers a given question.
Strategy doc visual outline
Quarterly strategy memos and 6-page strategy docs have implicit structure (theme → pillar → initiative). Mind Map exposes it so leadership reviewers can sanity-check coverage before approval.
Onboarding mental model
When joining a new project mid-stream, drop the project’s decision-record archive into Mind Map and use the output as the starting mental model. Faster ramp-up than reading docs in archive order.

How AI Mind Map works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop your PDF
    PDF up to 100 MB. Works best on docs with explicit structure (headings, sections); flat narrative docs (novels, long memos without headings) yield shallower trees.
  2. STEP 2
    We extract + tree-build
    Server-side text extraction, headings detection, then a structure-inference pass that groups content under hierarchical themes when the source lacks explicit headings. Routing layer picks the model based on cost-vs-quality fit.
  3. STEP 3
    Get text + JSON output
    Output is an indented text outline (paste into Notion, Workflowy, or Markdown directly) PLUS a JSON tree (importable into XMind, MindMeister, or any structured-data mind-map tool).

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

How deep does the tree go?
3 levels by default (root branch → sub-branch → leaf). Deeper docs get pruned to keep the output legible; shallow docs may only fill 2 levels. Custom depth is on the roadmap.
What if the doc has no headings?
The tool falls back to topic clustering — it identifies coherent concept groups in the prose and creates branches for each. Less accurate than heading-based extraction (it’s inferring structure that wasn’t explicit), but usable for most narrative docs.
Can I import the JSON into Miro or XMind?
JSON output uses a generic nested-tree shape ({title, children: []}). XMind and Miro both accept variants of this format. For tools with proprietary formats, you may need a small transform script.
Does it preserve page citations?
Each leaf node includes the source page reference. So you can navigate from a specific concept in the mindmap back to the page in the source PDF.
What’s the credit cost?
3 credits per mindmap. Cost is fixed regardless of doc size.
What if the structure is wrong?
Re-run; the inference is non-deterministic. For docs where structure matters a lot (policy manuals, certification course materials), pair with AI Summarize to verify the mindmap covers the doc’s major themes.

Want a Q&A index instead?

AI Generate FAQ produces a 6–10 question-and-answer set grounded in the same doc. Useful when readers don’t need the structure — they need the answers.

Try AI Generate FAQ

Related PDF tools

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